Module 6
Random Variables and Discrete
Probabilities Distribution
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Random Variables
A random variable is a function or rule that assigns a numbe
Module 5
Probability
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Probability
Probability is a numerical measure of the likelihood that an event will
occur.
Probability values are always
Module 11
Inference about a Population
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Inference About A Population
Population
Sample
Inference
Statistic
Parameter
We will develop techniques
Module 3
Numerical Descriptive Techniques
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Numerical Descriptive Techniques
Measures of Central Location
Mean, Median, Mode
Measures of Variabil
Module 10
Hypothesis Testing
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Statistical Inference
In Module 9 we introduced the first form of statistical inference:
estimation.
Hypothesis
Module 13
Regression Analysis and
Applications with Stata
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Part A.
Simple Regressions
Regression Analysis
Regression analysis is a statistical
Module 9
Introduction to Estimation
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Where we have been
Modules 6 and 7:
Binomial, Poisson, normal, and exponential distributions allow us to
ma
Module 8
Sampling Distribution
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Sampling Distributions
Recall in Module 6 we talked about expected value of a random
variable, also called as po
Module 7
Continuous Probabilities
Distribution
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Probability Density Functions
Unlike a discrete random variable which we studied in Module 6, a
Module 1
What is Statistics?
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Course Outline
Descriptive
Statistics
Probability
Theory
Statistical
Inference
1.2
What is Statistics?
Statistics
Module 2
Introduction to Data
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Warning
We will go through this module at a fast pace because most of the
topics in this module are something sh
Module 4
Uncertainty and Counting Rules
Economics 215 - Statistics
Spring 2016
Shih-Hsien Chuang
Department of Economics
University of Nebraska-Lincoln
Uncertainties
Why do we care about uncertainties?
Managers often base their decisions on an analysis
Kristen Jussel
Stata Assignment
Econ 215
(1i) salary= 772.426 + 11.746(ceoten)
(65.676) (6.148)
(ii) There are 0 CEOs in their first year, the longest tenure is 37.
(iii) .0097236 x 100= .97236 so about 1%
(2i) sleep= 3586.38 - .15(totwrk)
Number of obser
1. Students at Hope College were tested to see if they could determine the
difference between tap water and bottled water. Of the 63 students
tested, 42 correctly identified which was which. We will assume the
students tested are representative of all stu
ACCOUNTING 201
INTRODUCTION TO FINANCIAL ACCOUNTING
Fall 2016
OBJECTIVE
This is not a math class. It is true that accounting relies heavy on numbers however, the math is generally limited to addition,
subtraction and simple algebra. Accounting is as Warre
Antoljak 1
Tomislav Antoljak (Tommy)
English 150 Sec 001
Observational Essay Final Draft
24 October 2016
Being Different
When Rok says I must have a 4.0 GPA! he stands for it. And when on a Saturday
afternoon he shouts I cant hang out. I have a lot of stu
CHAPTER 1
3 sets of goals in communicating:
- instrumental communicationmessages aimed at accomplishing the task at hand
(How long do you want it to be? , Can you bring me that piece please?)
- relational communicationmessages that shape and reflect the w
Tomislav Antoljak
English 186
Carol Ochsner
18 April 2016
Argument Essay
Doping penalties
Many people consider sport as a healthy and pleasant activity which makes people
happy and fulfilled. However, professional sport is a totally different concept. Pro
Additional Review Problems for Exam II
1. Without simplifying, find the derivatives of the following functions:
a) g(x) = ln 4x 2 x
b) y = (2x 2 5)6 e x
c) f(x) =
2
ln x 2
3e 4x
2. Let f(x) = (8x 2 32) 4 . Find the open interval(s) on which f(x) is decrea
HIST 121 - The World Since 1500
Reaction Paper # 1
Prof. Bedross Der Matossian
TA: Sean Scanlon
Due on Thursday the 21st of January, 2015
"Based upon your reading in the Strayer textbook (Ch. 12) and of the
primary document Ma Huan's "The Country of Ku-Li
10 DISCOVERIES ABOUT U.S. COLLEGE GRADS
What did you learn from reading the article?
I learned that age on graduation from college does not matter. In my opinion, this is a very
important information, since lot of people underestimate older people on the
Econ 215-001 Spring 2017: Homework 3
Due Date: Friday (9:30 AM), January 27, 2017.
Name: Rok Krizaj ID: 74392933
Instructions:
You will need to download the data sets from either the Keller book website or
Blackboards Course Documents folder.
The question
English 186
Innovation!
The Plow
The Compass
The Wheel
Magnifying Lenses
The Printing Press
The Steam Engine
Refrigeration
The Lightbulb
Antibiotics
Fords Assembly Line
Pages 49 in our text has some good ideas about writing this essay. You may want to
do
1.2: Measuring the Strength of Evidence
Binary variable: categorical variable with only 2
outcomes (e.g. gender, flipping a coin, made/missed
free throw)
Symbols
e.g.
is the long run proportion (parameter)
=
1
3
is the long run probability of winning in
1.4: What Impacts Strength of Evidence
z=
statisticmean
standard error
1. Difference between the statistic and mean
Further away statistic is from the mean => larger
z-score (in absolute value) => smaller p-value
2. Sample size
Larger sample size => les
2.2 Inference for a Single Quantitative Variable
Categorical variables => make inferences to
population proportion (
Quantitative variable => make inferences to
population mean (
Definitions
Median: middle value of the data
o 9, 5, 7, 2, 8
2, 5, 7,
3.3: Confidence Intervals for a Single Mean
t=
x
s / n
Confidence Interval:
x Multiplier
Statistic Multiplier Standard Error
s
n
Valid for symmetric (bell-shaped) distribution
Must have sample size of at least 20
Example: A sample of 102 Honda Civics
3.1 and 3.4 Confidence Intervals
Example: Kissing Right?
A study showed that 80 out of 124 couples leaned their heads to
the right while kissing. Is the direction couples lean in actually
random?
H 0 : =0.50
H A : 0.50
^p=
p
80
=0.65
124
-value
0.0012
In
1.3: Alternative Measure of Strength of Evidence
Example: Heart Transplant Operations
15% of patients who received heart transplant operations
nationally have died. After 8 out of 10 people who
received heart transplants died at St. Georges hospital,
rese
4.1: Association and Confounding
Explanatory variable: variable that is explaining the
change
Response variable: variable that is being changed
o e.g. smoking (explanatory variable) => lung
cancer (response variable)
o e.g. high school GPAs (explanatory